A hybrid SEM-neural network method for modeling the academic satisfaction factors of architecture students

Aghaei, Soolmaz; Shahbazi, Yaser; Pirbabaei, Mohammadtaghi; Beyti, Hamed

doi:10.1016/j.caeai.2023.100122

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…The output layer produces the final results, which could be predictions, classifications, or any desired output [39,40]. An artificial neuron's learning ability is obtained by altering the weights in line with the specified learning algorithm [41]. ANNs can be classified into different types based on their architectural characteristics and applications.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…The target output data related to each structure in ANNs can be classified into different types based on their architectural characteristics and applications. Some common classifications of artificial neural networks include: feed-forward neural networks (FNNs), Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Radial Basis Function Networks (RBFNs), self-organizing maps (SOMs), deep neural networks (DNNs), Modular Neural Networks, Spiking Neural Networks (SNNs), and Autoencoders [41,[43][44][45]. In line with the objectives of this study, as in [9], a feed-forward backpropagation multilayer perceptron was used as a model for the basal artificial neural network.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

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Aesthetic Assessment of Free-Form Space Structures Using Machine Learning Based on the Expert’s Experiences

Shahbazi,

Ghofrani,

Pedrammehr

2023

Buildings

Self Cite

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Parametric form findings of free-form space structures and qualitative assessment of their aesthetics are among the concerns of architects. This study aims to evaluate the aesthetic aspect of these structures using ML algorithms based on the expert’s experiences. First, various datasets of forms were produced using a parametric algorithm of free-form space structures written in Grasshopper. Then, three multilayer perceptron ANN models were adjusted in their most optimal modes using the results of the preference test based on the aesthetic criteria including simplicity, complexity, and practicality. The results indicate that the ANN models can quantitatively evaluate the aesthetic value of free-form space structures.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Aesthetic Assessment of Free-Form Space Structures Using Machine Learning Based on the Expert’s Experiences

Shahbazi,

Ghofrani,

Pedrammehr

2023

Buildings

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, it oversimplifes the intricate decision-making process that exists in real-world situations, as mentioned by Hew et al [107]. To overcome this limitation, researchers have adopted a dual-stage analysis method that supplements PLS-SEM analysis with artifcial neural network (ANN) analysis, as demonstrated in studies by Leong et al [108], Wong et al [109], and Aghaei et al [110]. Also, ANN was employed to classify the relative efect of only signifcant predictors acquired from analysis of PLS-SEM [111,112].…”

Section: Data Processing Andmentioning

confidence: 99%

Predictors of Economics Students’ Behavioural Intention to Use Tablets: A Two-Staged Structural Equation Modelling–Artificial Neural Network Approach

Yidana

Arthur

Kaku

et al. 2023

Mobile Information Systems

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The government of Ghana, as part of its digitisation agenda, intends to provide all senior high school students in Ghana with tablets that are loaded with textbooks and other educational materials for their studies. This initiative is flaunted as one of the game-changing development that is yet to happen in the country. This study employed a modified unified theory of acceptance and use of technology model to examine Economics students’ behavioural intention to use tablets for learning. The study was quantitative research with a focus on a descriptive cross-sectional survey design. A total of 354 senior high school Economics students were selected for the study. A five-point Likert scale instrument was adapted as the data collection instrument for the study. A two-staged partial least square structural equation modelling–artificial neural network approach was used to analyse the data. The results revealed that effort expectancy, facilitating condition, social influence, and hedonic motivation had a significant positive influence on Economics students’ behavioural intention to use tablets for learning. However, habit and performance expectancy had no significant influence on Economics students’ behavioural intentions. Therefore, it was recommended that the implementation of the student-tablet policy should be hinged on social influence, effort expectancy, facilitating condition, and hedonic motivation (the student’s motivation) to use tablets due to internal satisfaction.

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Effects of digital readiness on digital competence of AEC companies: a dual-stage PLS-SEM-ANN analysis

Chen,

Chang-Richards,

Ling

et al. 2024

Building Research & Information

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A hybrid SEM-neural network method for modeling the academic satisfaction factors of architecture students

Cited by 8 publications

References 35 publications

Aesthetic Assessment of Free-Form Space Structures Using Machine Learning Based on the Expert’s Experiences

Aesthetic Assessment of Free-Form Space Structures Using Machine Learning Based on the Expert’s Experiences

Predictors of Economics Students’ Behavioural Intention to Use Tablets: A Two-Staged Structural Equation Modelling–Artificial Neural Network Approach

Effects of digital readiness on digital competence of AEC companies: a dual-stage PLS-SEM-ANN analysis

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